1 / 28

12 December 2010

12 December 2010. Smart data selector. Moti Abu & Roee Ben Halevi. Supervisors: Prof. Mark Last, Mr. Hanan Friedman. Telemetry. Velocities Vibrations Pressures ……………. Bit Errors. SNR (dB). Bit Errors. Solutions.

meli
Télécharger la présentation

12 December 2010

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 12 December 2010 Smart data selector Moti Abu & Roee Ben Halevi Supervisors: Prof. Mark Last, Mr. Hanan Friedman

  2. Telemetry Velocities Vibrations Pressures ……………..

  3. Bit Errors SNR (dB)

  4. Bit Errors

  5. Solutions • Best Source SelectorSelects the best source based on received signallevel. • Best Data SelectorSelects the best data based on SNR and receivedsignal level. • Correlated Source SelectorSelects the best data bits based on best data fit. • Smart Source SelectorCombination of all the above.

  6. Solutions

  7. Solutions

  8. 12 December 2010 Survey

  9. 12 December 2010 Requirements

  10. 12 December 2010 Requirements

  11. 12 December 2010 Requirements

  12. 12 December 2010 Requirements The quest for the best similarity value…

  13. 12 December 2010 Use Cases

  14. 12 December 2010 Use Cases • Use Case 2 [UC2]: generate master • Primary Actor: Telemetry technician (or common user). • Interests: The technician wants to generate a master output from source inputs. • Pre-conditions: User opened a new job, loaded sources and defined aconfiguration. • Post-conditions: The master is generated and saved in the job directory with metadata information. • Main success scenario: 1. User clicks on "play" icon.2. An estimate for the execution time is displayed for the user and he is asked to confirm.3. After confirmation, master output and metadata information are generated according to user configuration and saved in job directory. • Main fail scenario: 1. User clicks on "play" icon.2. An estimate for the execution time is displayed for the user and he is asked to confirm.3. User cancels master generation.

  15. 12 December 2010 Use Cases • Use Case 3 [UC3]: Integrate input • Primary Actor: Telemetry technician. • Interests: The technician wants to integrate input source to master output. • Pre-conditions: Configuration file, master output and metadata information are present in the job directory. User loaded inputs. • Post-conditions: The new master is generated and saved in the job directory with new metadata information. • Main success scenario: 1. User clicks on "integrate sources" icon.2. An estimate for the execution time is displayed for the user and he is asked to confirm.3. After confirmation, new master output and metadata information are generated according to user configuration and saved in job directory. • Main fail scenario: 1. User clicks on "integrate sources" icon.2. An estimate for the execution time is displayed for the user and he is asked to confirm.3. User cancels integration

  16. 12 December 2010 Proposed Solution

  17. 12 December 2010 Dataflow in Solution Experimental Aircraft Experimental aircraft transmits data to ground receivers Ground Receivers Several ground receivers record the data Preprocess unit (XXX-Telemetry records creator) The raw data that was recorded in each receiver is formatted and metadata files are created Smart Data Selector (SDS) Integrates all inputs to one master record that is as close as possible to the original data Analysis tools and QuickView Master record is used for analysis of flight and plotting

  18. 12 December 2010 Software Context Major Minor Minor Minor Minor Minor Minor Minor Minor Minor Minor Minor Minor Major Minor Minor Minor Minor Minor Minor Minor Minor Minor Minor Minor Minor Major Minor Minor Minor Minor Minor Minor Minor Minor Minor Minor Minor Minor

  19. 12 December 2010 Survey

  20. 12 December 2010 Software Context Output to GUI Best record Pack Master Unit Slave Reader Selecting Algorithm Master Readers Scheduler Master Builder Best raw data Best metadata Slave Unit Slave Unit Slave Unit

  21. 12 December 2010 Master Unit – Part A Top Rated Record Threads Raw Data Meta Data Master Reader Part I Read Master Reader 1 Meta Data Part [N/2] Read Master Reader N/2]] Part N Read Master Reader N

  22. 12 December 2010 Have Bad Sync? OR Have Bad CRC? Pack as Bad Minor! Have Sync? Yes Have CRC? Yes Pack as Good Minor! Master Readers Meta Minor Meta Minor Raw Minor Raw Minor Minor Bad Minor Bad Minors Queue Good Minors Queue Builder Master Record

  23. 12 December 2010 Good Minors Queue Bad Minors Entry Queue Scheduler Bad signature Packet Packet Packet Packet Task Queue Task Queue Task Queue Slave Task Queue Slave Slave Slave Apply Algorithm Apply Algorithm Good Minor Good Minor

  24. Slave Minor Signature Raw Data Records Meta Data Records Slave Reader All Records have bad minor??  Apply algorithm . • Find Good Minor In Others Records? • Yes! • Send Location to • Builder Algorithm Machine Algorithms can be added and/or changed by user (Support Reflection mechanism )

  25. Algorithm examples: - BitVoting. -Pattern recognition . -User Algorithm. Station B Origin data 1 0 0 1 0 1 0 1 0 0 1 0 1 0 1 1 0 1 0 1 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 BitVote Station A Station C

  26. Good Minors Queue Good Minor Good Minor Builder Master Record To GUI

  27. 12 December 2010 Block Diagram

  28. 12 December 2010 QUESTIONS…

More Related